NCT07414576

AI-driven Total Parenteral Nutrition Platform

Clinical Decision Support for Total Parenteral Nutrition Constituents in Neonatal Intensive Care Unit (NICU) Patients: A Pilot Study

• Status: Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 260 (estimated)
• Sponsor: Takeoff41, Inc. · Industry
• Sex: All
• Age: 6 Months
• Healthy volunteers: Not accepted

Summary

This study tests whether an artificial intelligence (AI) tool can help doctors order total parenteral nutrition (TPN) for babies in the neonatal intensive care unit (NICU). Premature babies often cannot eat by mouth and need nutrition delivered through an IV. Ordering TPN is complex, time-consuming, and mistakes can happen. This study will test an AI tool that suggests TPN formulas to doctors based on each baby's lab values and health information. Doctors can accept, change, or reject the suggestions at any time. The main goal is to measure how often doctors accept the AI suggestions. The study will also track time to complete TPN orders, weight changes, days on TPN, whether lab values stay in normal ranges, provider satisfaction, and baby health outcomes including complications such as lung disease, brain bleeding, infections, and other conditions common in premature babies. Babies admitted to the NICU who need TPN may participate if their doctors agree to use the tool. Each baby will be in the study while they need TPN, typically about 14 days. The AI tool only makes suggestions and does not replace doctor decision-making. All other care remains the same as standard practice.

Detailed description

Our AI-driven TPN (TPN2.0) platform is a combination of AI and a premade set of TPN units. The AI is used to formulate and assign the optimal TPN unit to each infant, given their daily profile and lab test values. It is driven by decades of data, including our published morbidity risks, basic demographics, and routinely collected lab test values. The approach will save staff time and eliminate high errors in the current TPN ordering process. Our pilot will be deployed as a clinical decision support tool that only makes recommendations, and doctors can always override it. As such, this minimally affects the current practice. We aim to enroll 260 neonates in this pilot study. The primary outcome is physician acceptance rate of TPN2.0 recommendations. Secondary outcomes include time to complete TPN orders, change in weight z-score, days on TPN, lab value abnormalities (values outside normal range), provider satisfaction, and a composite morbidity index comprising rates of bronchopulmonary dysplasia, necrotizing enterocolitis, retinopathy of prematurity, respiratory distress syndrome, congenital heart disease, sepsis, anemia, intraventricular hemorrhage, cholestasis, jaundice, pulmonary hemorrhage, pulmonary hypertension, readmission during the study, and mortality.

Conditions

• Intestinal Failure

Interventions

Type	Name	Description
DEVICE	AI-driven total parenteral nutrition (TPN)	An AI-driven clinical decision support (CDS) software integrated with EHR system that provides TPN composition recommendations to NICU providers. The tool uses patient lab values, basic profile (days since birth, weight, gestational age), and physician inputs to suggest TPN components. Providers can accept, modify, or decline if needed. The final prescribing authority remains with the providers. The intervention targets provider workflow efficiency while maintaining precision and equivalent patient outcomes (including labs and long-term adverse outcomes).

Timeline

• Start date: 2026-01-21
• Primary completion: 2027-02-21
• Completion: 2027-02-21
• First posted: 2026-02-17
• Last updated: 2026-02-17

Locations

1 site across 1 country: United States